{"689263":{"#nid":"689263","#data":{"type":"news","title":"Transformer Explainer Shows How AI is More Math than Human","body":[{"value":"\u003Cp\u003EWhile people use search engines, chatbots, and generative artificial intelligence tools every day, most don\u2019t know how they work. This sets unrealistic expectations for AI and leads to misuse. It also slows progress toward building new AI applications.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGeorgia Tech researchers are making AI easier to understand through their work on Transformer Explainer. The free, online tool shows non-experts how ChatGPT, Claude, and other large language models (LLMs) process language.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/poloclub.github.io\/transformer-explainer\/\u0022\u003ETransformer Explainer\u003C\/a\u003E is easy to use and runs on any web browser. It quickly went viral after its debut, reaching 150,000 users in its first three months. More than 563,000 people worldwide have used the tool so far.\u003C\/p\u003E\u003Cp\u003EGlobal interest in Transformer Explainer continues when the team presents the tool at the 2026 Conference on Human Factors in Computing Systems (\u003Ca href=\u0022https:\/\/chi2026.acm.org\/\u0022\u003ECHI 2026\u003C\/a\u003E). CHI, the world\u2019s most prestigious conference on human-computer interaction, will take place in Barcelona, April 13-17.\u003C\/p\u003E\u003Cp\u003E\u201cThere are moments when LLMs can seem almost like a person with their own will and personality, and that misperception has real consequences. For example, there have been cases where teenagers have made poor decisions based on conversations with LLMs,\u201d said Ph.D. student\u0026nbsp;\u003Ca href=\u0022https:\/\/aereeeee.github.io\/\u0022\u003EAeree Cho\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003E\u201cUnderstanding that an LLM is fundamentally a model that predicts the probability distribution of the next token helps users avoid taking its outputs as absolute. What you put in shapes what comes out, and that understanding helps people engage with AI more carefully and critically.\u201d\u003C\/p\u003E\u003Cp\u003EA transformer is a neural network architecture that changes data input sequence into an output. Text, audio, and images are forms of processed data, which is why transformers are common in generative AI models. They do this by learning context and tracking mathematical relationships between sequence components.\u003C\/p\u003E\u003Cp\u003ETransformer Explainer demystifies how transformers work. The platform uses visualization and interaction to show, step by step, how text flows through a model and produces predictions.\u003C\/p\u003E\u003Cp\u003EUsing this approach, Transformer Explainer impacts the AI landscape in four main ways:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EIt counters hype and misconceptions surrounding AI by showing how transformers work.\u003C\/li\u003E\u003Cli\u003EIt improves AI literacy among users by removing technical barriers and lowering the entry for learning about AI.\u003C\/li\u003E\u003Cli\u003EIt expands AI education by helping instructors teach AI mechanisms without extensive setup or computing resources.\u003C\/li\u003E\u003Cli\u003EIt influences future development of AI tools and educational techniques by providing a blueprint for interpretable AI systems.\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003E\u201cWhen I first learned about transformers, I felt overwhelmed. A transformer model has many parts, each with its own complex math. Existing resources typically present all this information at once, making it difficult to see how everything fits together,\u201d said\u0026nbsp;\u003Ca href=\u0022https:\/\/gracekimcy.github.io\/\u0022\u003EGrace Kim\u003C\/a\u003E, a dual B.S.\/M.S. computer science student.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cBy leveraging interactive visualization, we use levels of abstraction to first show the big picture of the entire model. Then users click into individual parts to reveal the underlying details and math. This way, Transformer Explainer makes learning far less intimidating.\u201d\u003C\/p\u003E\u003Cp\u003EMany users don\u2019t know what transformers are or how they work. The Georgia Tech team found that people often misunderstand AI. Some label AI with human-like characteristics, such as creativity. Others even describe it as working like magic.\u003C\/p\u003E\u003Cp\u003EFurthermore, barriers make it hard for students interested in transformers to start learning. Tutorials tend to be too technical and overwhelm beginners with math and code. While visualization tools exist, these often target more advanced AI experts.\u003C\/p\u003E\u003Cp\u003ETransformer Explainer overcomes these obstacles through its interactive, user-focused platform. It runs a familiar GPT model directly in any web browser, requiring no installation or special hardware.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EUsers can enter their own text and watch the model predict the next word in real time. Sankey-style diagrams show how information moves through embeddings, attention heads, and transformer blocks.\u003C\/p\u003E\u003Cp\u003EThe platform also lets users switch between high-level concepts and detailed math. By adjusting temperature settings, users can see how randomness affects predictions. This reveals how probabilities drive AI outputs, rather than creativity.\u003C\/p\u003E\u003Cp\u003E\u201cMillions of people around the world interact with transformer-driven AI. We believe that it is crucial to bridge the gap between day-to-day user experience and the models\u0027 technical reality, ensuring these tools are not misinterpreted as human-like or seen as sentient,\u201d said Ph.D. student\u0026nbsp;\u003Ca href=\u0022https:\/\/www.alexkarpekov.com\/\u0022\u003EAlex Karpekov\u003C\/a\u003E.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cExplaining the architecture helps users recognize that language generated by models is a product of computation, leading to a more grounded engagement with the technology.\u201d\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ECho, Karpekov, and Kim led the development of Transformer Explainer. Ph.D. students\u0026nbsp;\u003Ca href=\u0022https:\/\/alechelbling.com\/\u0022\u003EAlex Helbling\u003C\/a\u003E,\u0026nbsp;\u003Ca href=\u0022https:\/\/seongmin.xyz\/\u0022\u003ESeongmin Lee\u003C\/a\u003E,\u0026nbsp;\u003Ca href=\u0022https:\/\/bhoov.com\/\u0022\u003EBen Hoover\u003C\/a\u003E, and alumnus\u0026nbsp;\u003Ca href=\u0022https:\/\/zijie.wang\/\u0022\u003EZijie (Jay) Wang\u003C\/a\u003E assisted on the project.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EProfessor\u0026nbsp;\u003Ca href=\u0022https:\/\/poloclub.github.io\/polochau\/\u0022\u003EPolo Chau\u003C\/a\u003E supervised the group and their work. His lab focuses on data science, human-centered AI, and visualization for social good.\u003C\/p\u003E\u003Cp\u003EAcceptance at CHI 2026 stems from the team winning the best poster award at the 2024 IEEE Visualization Conference. This recognition from one of the top venues in visualization research highlights Transformer Explainer\u2019s effectiveness in teaching how transformers work.\u003C\/p\u003E\u003Cp\u003E\u201cTransformer Explainer has reached over half a million learners worldwide,\u201d said Chau, a faculty member in the School of Computational Science and Engineering.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cI\u0027m thrilled to see it extend Georgia Tech\u0027s mission of expanding access to higher education, now to anyone with a web browser.\u201d\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EWhile people use search engines, chatbots, and generative artificial intelligence tools every day, most don\u2019t know how they work. This sets unrealistic expectations for AI and leads to misuse. It also slows progress toward building new AI applications.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGeorgia Tech researchers are making AI easier to understand through their work on Transformer Explainer. The free, online tool shows non-experts how ChatGPT, Claude, and other large language models (LLMs) process language.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/poloclub.github.io\/transformer-explainer\/\u0022\u003ETransformer Explainer\u003C\/a\u003E is easy to use and runs on any web browser. It quickly went viral after its debut, reaching 150,000 users in its first three months. More than 563,000 people worldwide have used the tool so far.\u003C\/p\u003E\u003Cp\u003EGlobal interest in Transformer Explainer continues when the team presents the tool at the 2026 Conference on Human Factors in Computing Systems (\u003Ca href=\u0022https:\/\/chi2026.acm.org\/\u0022\u003ECHI 2026\u003C\/a\u003E). CHI, the world\u2019s most prestigious conference on human-computer interaction, will take place in Barcelona, April 13-17.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia Tech researchers are making AI easier to understand through their work on Transformer Explainer. The free, online tool shows non-experts how ChatGPT, Claude, and other large language models (LLMs) process language, improving AI literacy."}],"uid":"36319","created_gmt":"2026-03-31 16:42:57","changed_gmt":"2026-04-02 13:48:23","author":"Bryant Wine","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2026-03-31T00:00:00-04:00","iso_date":"2026-03-31T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"679798":{"id":"679798","type":"image","title":"Transformer-Explainer-Head-Image.jpg","body":null,"created":"1774975392","gmt_created":"2026-03-31 16:43:12","changed":"1774975392","gmt_changed":"2026-03-31 16:43:12","alt":"CHI 2026 Transformer Explainer","file":{"fid":"264002","name":"Transformer-Explainer-Head-Image.jpg","image_path":"\/sites\/default\/files\/2026\/03\/31\/Transformer-Explainer-Head-Image.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/03\/31\/Transformer-Explainer-Head-Image.jpg","mime":"image\/jpeg","size":120484,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/03\/31\/Transformer-Explainer-Head-Image.jpg?itok=eryBAi-R"}},"679799":{"id":"679799","type":"image","title":"Transformer-Explainer-Text-Image.jpg","body":null,"created":"1774975428","gmt_created":"2026-03-31 16:43:48","changed":"1774975428","gmt_changed":"2026-03-31 16:43:48","alt":"CHI 2026 Transformer Explainer","file":{"fid":"264003","name":"Transformer-Explainer-Text-Image.jpg","image_path":"\/sites\/default\/files\/2026\/03\/31\/Transformer-Explainer-Text-Image.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/03\/31\/Transformer-Explainer-Text-Image.jpg","mime":"image\/jpeg","size":69012,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/03\/31\/Transformer-Explainer-Text-Image.jpg?itok=0B-WDInX"}}},"media_ids":["679798","679799"],"related_links":[{"url":"https:\/\/www.cc.gatech.edu\/news\/transformer-explainer-shows-how-ai-more-math-human","title":"Transformer Explainer Shows How AI is More Math than Human"}],"groups":[{"id":"47223","name":"College of Computing"},{"id":"1188","name":"Research Horizons"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[{"id":"130","name":"Alumni"},{"id":"194606","name":"Artificial Intelligence"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"135","name":"Research"},{"id":"134","name":"Student and Faculty"},{"id":"8862","name":"Student Research"}],"keywords":[{"id":"654","name":"College of Computing"},{"id":"166983","name":"School of Computational Science and Engineering"},{"id":"187915","name":"go-researchnews"},{"id":"9153","name":"Research Horizons"},{"id":"10199","name":"Daily Digest"},{"id":"181991","name":"Georgia Tech News Center"},{"id":"170447","name":"Institute for Data Engineering and Science"},{"id":"176858","name":"machine learning center"},{"id":"9167","name":"machine learning"},{"id":"187812","name":"artificial intelligence (AI)"},{"id":"14646","name":"human-computer interaction"},{"id":"192863","name":"go-ai"},{"id":"194384","name":"Tech AI"}],"core_research_areas":[{"id":"193655","name":"Artificial Intelligence at Georgia Tech"},{"id":"39431","name":"Data Engineering and Science"},{"id":"39501","name":"People and Technology"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EBryant Wine, Communications Officer\u003Cbr\u003E\u003Ca href=\u0022mailto:bryant.wine@cc.gatech.edu\u0022\u003Ebryant.wine@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}